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c22b544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
import os
import copy
from model import CLAPEncoder
from audio_dataset import AudioCapsDataset
from loss import CLAPLoss
from collate import CollateFN
from rir_dataset import RIRDataset
from util import set_seed
from generate_caption import meta_to_caption
def train(
model,
dataloader,
optimizer,
loss_fn,
collate_fn,
epoch=0,
log_f=None,
):
model.train()
total_loss = 0
loss_num = 0
for batch_idx, batch in enumerate(tqdm(dataloader)):
(waveforms, metas) = collate_fn(batch)
captions = [meta_to_caption(meta) for meta in metas]
optimizer.zero_grad()
# Forward
clap_output = model(audio=waveforms, text=captions)
# Compute loss
loss = loss_fn(clap_output, metas, model.logit_scale)
# Backprop
loss.backward()
optimizer.step()
total_loss += loss.item()
loss_num += 1
if batch_idx % 10 == 0:
log_line = f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}"
print(log_line)
if log_f is not None:
log_f.write(log_line + "\n")
avg_loss = total_loss / loss_num
return avg_loss
def validate(model, dataloader, loss_fn, collate_fn):
model.eval()
with torch.no_grad():
total_loss = 0
loss_num = 0
for batch_idx, batch in enumerate(tqdm(dataloader)):
(waveforms, metas) = collate_fn(batch)
captions = [meta_to_caption(meta) for meta in metas]
# Forward
clap_output = model(audio=waveforms, text=captions)
# Compute loss
loss = loss_fn(clap_output, metas, model.logit_scale)
total_loss += loss.item()
loss_num += 1
avg_loss = total_loss / loss_num
return avg_loss
def main():
batch_size = 128
device = "cuda" if torch.cuda.is_available() else "cpu"
set_seed()
# Config
out_dir = "output"
config = {
"wav_dir": "data/wav/",
"csv_path": "data/fixed_audiocaps2.0/train.csv",
"val_csv_path": "data/fixed_audiocaps2.0/val.csv",
"batch_size": batch_size,
"epochs": 50,
"path": {
"log_dir": os.path.join(out_dir, "log"),
"ckpt_dir": os.path.join(out_dir, "ckpt"),
},
}
for d in config["path"].values():
os.makedirs(d, exist_ok=True)
val_config = copy.deepcopy(config)
val_config["csv_path"] = val_config["val_csv_path"]
# Train Dataset & Dataloader
train_rir_dataset = RIRDataset("train")
train_audio_dataset = AudioCapsDataset(config)
train_collate_fn = CollateFN(train_rir_dataset, device)
train_dataloader = DataLoader(
train_audio_dataset, batch_size=config["batch_size"],
shuffle=True, num_workers=16, drop_last=True, collate_fn=lambda x:x)
# Val Dataset & Dataloader
val_rir_dataset = RIRDataset("val")
val_audio_dataset = AudioCapsDataset(val_config)
val_collate_fn = CollateFN(val_rir_dataset, device)
val_dataloader = DataLoader(
val_audio_dataset, batch_size=val_config["batch_size"],
shuffle=True, num_workers=16, drop_last=True, collate_fn=lambda x:x)
# Model
model = CLAPEncoder().to(device)
model.load_default_state_dict()
# Loss & Optimizer
loss_fn = CLAPLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-5)
# Open log file
train_log_f = open(os.path.join(config["path"]["log_dir"], "train_log.txt"), "w")
val_log_f = open(os.path.join(config["path"]["log_dir"], "val_log.txt"), "w")
# Training loop
for epoch in range(config["epochs"]):
avg_loss = train(
model, train_dataloader, optimizer, loss_fn, train_collate_fn, epoch, train_log_f
)
val_avg_loss = validate(
model, val_dataloader, loss_fn, val_collate_fn
)
logit_scale_value = model.logit_scale.exp().item()
log_line = f"===> Epoch {epoch} Avg Loss: {avg_loss:.4f}, Val Loss: {val_avg_loss:.4f}, Temperature: {logit_scale_value:.4f}"
print(log_line)
train_log_f.write(log_line + "\n")
log_line = f"Epoch {epoch} Val Loss: {val_avg_loss:.4f}, Temperature: {logit_scale_value:.4f}"
val_log_f.write(log_line + "\n")
# Save model
save_path = os.path.join(config["path"]["ckpt_dir"], f"model_epoch_{epoch}.pt")
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, save_path)
if __name__ == "__main__":
main()
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